Field of the Invention
The present invention relates to methods and apparatus for assisting in interpretation of images, particularly patient medical images, and particularly hybrid anatomical-functional patient medical image datasets.
Description of the Prior Art
It is well known that medical image data can be captured in multiple modalities and the image data from these modalities aligned to assist with detection and interpretation of multiple features. Typically, an anatomical imaging modality such as MRI or CT provides data on the position, size and shape of organs, while a functional imaging modality such as PET or SPECT provides information on the state of the organs, for example indicating lesions by their increased uptake of an introduced tracer. As images in these differing modalities must be captured by correspondingly differing equipment, alignment must be carried out to ensure correct registration between the sets of image data, even if the two sets of image data were captured at the same time on a multimodality imaging equipment. Alignment methods are known in the art, and do not form part of the present invention.
In a typical scenario, a patient is imaged several times over an extended period of time, lasting several months. The present invention is directed to methods and systems to aid a user in retrieving similar findings from stored image records when presented with a new image dataset.
A finding in a multi-modality patient medical image dataset is typically defined by its intensity, appearance and location, as well as the patient's clinical history and referring indication, that is, the subject of a question that a referring physician (e.g., oncologist) wants answering. For example, for a patient with lung cancer, the referring indication may be the stage of the cancer, or the efficacy of the treatment.
As part of a clinical read, a user will classify their findings based on such features, in combination with any other findings in the image and non-image features, such as patient history, histopathology. Retrieval and presentation of similar lesions which have already been classified may aid a user in assessment of lesions in a newly-presented image dataset. Further information which may be available regarding these similar lesions, such as treatment strategy and patient response, may also be useful to a user in planning patient management decisions.
In another application, retrieval of similar stored information regarding similar lesions from earlier datasets may be useful to a user in determining an appropriate label for a selected lesion. For example, a newly-presented image representing a lesion may be compared to earlier representations of benign and malignant lesions to assist a user in classification or annotation of the newly-presented lesion.
In order to be useful in a medical imaging environment, the identification and retrieval of records of similar lesions must occur rapidly, while the information they provide should maintain high quality and pertinence to the case.
Some attempts have been made in the past to provide content-based image retrieval (CBIR) systems, but the complexity of the task and the volume of data involved have made this a technically challenging task. Known CBIR systems typically take as an input a user-specified image that includes a feature of interest. Quantitative descriptors are then computed for the image and are tested for similarity against a pre-processed database of images. Such image-intensity descriptors can be computed quickly, but the retrieval of identified image data of matched lesions is typically time-consuming due to the quantity of image data involved, and the transmission speeds of networks carrying the data.
The image-intensity descriptors used by known CBIR systems are, in themselves, unlikely to encode additional clinically-important properties, such as the anatomical location of the lesion, and its relative position to organ boundaries. Ex-5 traction of such properties may be performed by known means and methods, such as detecting lesion position relative to anatomical landmarks and lesion segmentation. A processing time of the image data will be lengthened by the extraction step. Pre-computation would be required to meet performance 10 requirements.